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Rezky Yunita
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Jurnal Meteorologi dan Geofisika
Published by Pusat Penelitian dan Pengembangan, Badan Meteorologi Klimatologi dan Geofisika
ISSN : 14113082     EISSN : 25275372     DOI : https://doi.org/10.31172/jmg
Core Subject : Science,
Earth & Planetary Sciences Energy Physics
Jurnal Meteorologi dan Geofisika (JMG) is a scientific research journal published by the Research and Development Center of the Meteorology, Climatology, and Geophysics Agency (BMKG) as a means to publish research and development achievements in Meteorology, Climatology, Air Quality and Geophysics.
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 178 Documents
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Network-Based Equity Evaluation of Tsunami Evacuation Access for a Megathrust Scenario in Palabuhanratu: English Sudibyo, Reno; Kurniadi, Anwar; Subiyanto, Adi; Ramadhan, Fajar Gilang
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1196

Abstract

We present a network-based equity evaluation of tsunami evacuation access for a megathrust scenario in Palabuhanratu, quantifying both individual safety attainment and the spatial distribution of access. By overlaying physics-based inundation data with a road graph, we compute multimodal time-to-safety and isochrones, summarizing village-level access through overall reachability (RR), Gini, and hazard-weighted Gini (Gini*) indices. Evacuation time allowances (ETAs) are set at 22, 18, and 15 minutes—validated against site-specific arrival modeling and real-world departure observations from the 2024 Noto event—revealing a critical temporal tipping point. While an ETA of 22 minutes ensures total reachability (RR=1.00) with low inequality, tightening the window to 18 and 15 minutes sharply reduces RR and increases Gini* scores. Furthermore, the addition of an alternative Tsunami Evacuation Area (TEA) at Smile Hill yields localized time savings and minor gains in specific clusters at 22 minutes, yet provides no systemwide benefit at shorter ETAs, indicating that time scarcity dominates access during tight windows. Methodologically, this study employs "beat-the-wave" logic and least-cost routing on OSMnx/NetworkX graphs, offering a reproducible screening tool that integrates access, fairness, and hazard emphasis for TEA design under time-critical evacuation constraints.
the CORRELATION BETWEEN SEA SURFACE TEMPERATURE AND CONVECTIVE CLOUDS IN AMBON ISLANDS Pasaribu, Puput Mustika; Tubalawony, Simon; Masrikat, Julius Anthon Nicolas
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1085

Abstract

Sea surface temperature (SST) plays a key role in modulating tropical convection, yet its influence on the vertical structure of convective clouds in island-based, anti-monsoonal regions remains poorly quantified. This study examines the relationship between SST variability and convective cloud characteristics over the coastal waters of Ambon Island during 2023. Daily SST data were obtained from ERA5 reanalysis, while convective parameters Convective Condensation Level (CCL) and Equilibrium Level (EL) were derived from twice-daily radiosonde observations. Owing to non-normal distributions and serial autocorrelation, Spearman rank correlation was applied with effective sample size (ESS) correction and bootstrap confidence intervals. Results show that SST exhibits a pronounced seasonal cycle primarily governed by monsoonal forcing. SST displays a moderate positive correlation with CCL (ρ = 0.532-0.580) and a consistently strong correlation with EL across all stations (ρ = 0.770-0.778; p_adj < 0.001), indicating a stronger SST control on convective depth than on cloud-base height. Although large-scale climate modes (ENSO, IOD, and MJO) contribute to short-term variability, seasonal monsoonal forcing remains the dominant modulator of SST-convection coupling. These findings represent robust statistical associations and highlight the importance of ocean-atmosphere coupling in regulating convective cloud structure in tropical maritime island environments.
Determining Monsoon Onset Dates in Makassar Using Rainfall Anomalies and Moisture Source Trajectory Analysis (1991–2020) Hutauruk, Rheinhart; Hadi, Tri Wahyu; Muharsyah, Robi; Yolanda, Selvy
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1162

Abstract

Makassar exhibits a typical monsoonal rainfall regime, characterized by a strong annual cycle with peak rainfall occurring in January–February. Understanding the onset of the rainy season in this region is crucial for water resource management and disaster preparedness, yet previous studies have generally relied only on rainfall-based criteria with coarse temporal resolution. This study aims to determine the onset date of the rainy season in Makassar by combining local rainfall anomalies with regional-scale moisture-source trajectories. Daily rainfall data for 1991–2020 were analyzed using harmonic reconstruction to identify the climatological peak of the monsoon season, which then guided the moisture trajectory analysis. The results show that most rainy-season onsets occur in November–December, with high interannual variability influenced by large-scale climate drivers such as ENSO. Moisture transport during the peak rainy months is predominantly derived from the Northern Maritime (58.8%) and Tropical Maritime (40.5%) sources, highlighting the essential role of cross-equatorial water-vapor advection. In addition, changes in zonal wind direction at 850 hPa consistently coincide with the onset, providing an independent dynamical indicator of the transition from dry to wet phase. By explicitly linking rainfall anomalies with the timing of dynamical shifts and dominant moisture pathways, this approach reduces ambiguities commonly found in rainfall-only methods and produces onset estimates that align more closely with regional atmospheric dynamics. Compared to previous rainfall-only approaches, this combined local–regional method provides a more representative onset estimate at daily resolution, offering new insight into the mechanisms of monsoon rainfall in coastal areas of eastern Indonesia.
Comparative Analysis of Weather Radar Signatures of Puting Beliung in Indonesia Kiki; Koesmaryono, Yonny; Hidayat, Rahmat; Sukma Permana, Donaldi; Perdinan
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1180

Abstract

This study presents a comprehensive radar-based analysis of Puting Beliung (PB), Indonesia’s localized tornado phenomenon, using multiple weather radar–derived products. A comparative analysis of ten PB cases was conducted to identify consistent meteorological signatures and variations in tropical storm behavior, motivated by recent observations indicating an increasing frequency of PB events in Indonesia and the need for improved detection methods. Analysis using Rainbow software reveals consistently high reflectivity values ranging from 35 to 60 dBZ, with diverse echo patterns, among which the hook echo is the most dominant. Physical parameters show horizontal wind speeds of 10–30 knots at an altitude of 4 km, horizontal shear of 5–10 m s⁻¹ km⁻¹, and vertical shear of 1–10 m s⁻¹ km⁻¹, while spectral width analysis indicates moderate turbulence with values around 3 m s⁻¹. The Tornadic Vortex Detection (TVD) product identifies potential vortex signatures at six locations, with detected heights ranging from 1.2 to 3.1 km. This study represents the first comprehensive application of multiple radar products for PB characterization in Indonesia and identifies CMAX, HWIND, HSHEAR, and TVD as the most effective products for PB detection and monitoring. These findings provide essential baseline criteria for the development of radar-based early warning systems tailored to Indonesia’s tropical environment, with the potential to reduce the socioeconomic impacts of PB events through improved detection and prediction capabilities.
Application of Remote Sensing for Long-Term Analysis of Environmental Criticality Indices in Kendari City Septianto Aldiansyah; Amniar Ati; Fitriyani Saudi
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1151

Abstract

The decline in vegetation cover has a crucial impact on ecological quality and function, as seen on critical land. Kendari City experiences fairly rapid population growth, being the capital of Southeast Sulawesi Province. This study analyzes long-term changes in Kendari City's environmental quality using the Environmental Criticality Index (ECI) derived from Multitemporal Landsat Satellite Imagery. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) can provide information on environmental criticality through the contrast difference between vegetation as a cooling agent and surface temperature, which is often amplified by built-up areas. The results of the study show that the concentration of ECI occurs in the city center area that stretches along Kendari Bay. The moderate ECI experienced regional fluctuations in the 1991-2002, 2001-2014, and 2014-2023 periods, namely -8.43%, +2.28%, and -20.35%, respectively. Meanwhile, the high class experienced gradual expansion, namely +3.38%, +5.12%, and +1.03%, respectively. The 2001-2014 period was the peak phase of ECI changes, with a tendency for environmental quality to decline. The Urban-Rural Gradient shows that the highest ECI value occurs in urban areas and decreases as it moves towards the suburbs, where there are still rural areas. The correlation test shows a strong relationship between them and indicates a significant influence on ECI. These findings underscore the importance of sustainable reforestation strategies in reducing urban environmental pressures.
Development of an Automated Recording and Analysis System for Radiosonde (RASON) Data Using Random Forest Method for the Meteorological Station of BMKG Tarakan City Farhan Muhammad Nabil; Arif Fadllullah
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1157

Abstract

At the BMKG (Indonesia's Meteorology, Climatology and Geophysics Agency) station in Tarakan City, manual recording and analysis of radiosonde observations pose challenges to efficiency and accuracy. The workflow includes automatic processing and classification of data. We designed a tool called the Radiosonde Data Recap and Analysis System (RASON) using the Random Forest model. This tool was designed in Python using PyQt5 (Python’s GUI framework), enabling data import, processing, and visualization. For the Random Forest classifier, the historical radiosonde datasets (844 soundings after cleaning) were used to train and validate between 2022 and 2023, and the independent dataset (733 soundings) from 2024 was used to evaluate out-of-sample performance. While it took more than 10 minutes to manually add the point data, its trained sample took up 20 points in 8 seconds. On several significant atmospheric stability metrics, including K Index (KI), Lifted Index (LI), Showalter Index (SI), Total Totals (TT), and Convective Available Potential Energy (CAPE), the Random Forest classification model presents an almost ideal classification score. Evaluation verified all performance metrics on each index as given; for instance, Precision, Recall, and F1-Score of 1.0000 were attained in each metric KI_Class and TT_Class, LI_Class and CAPE_Class achieved better than 0.99, SI_Class had an F1-Score of 0.9267 due to low precision compared to other indices. Each of the five indexes was achieved via overall classification (AllData_Class) using majority voting, producing Precision=0.9030, Recall=0.9877, and F1-Score=0.9369. An average score of 90 was obtained on the System Usability Scale (SUS), indicating the highest level of user satisfaction and usability. This is how it enables the processing of increasing amounts of radiosonde data to enhance decision-making for effective weather forecasting at BMKG Tarakan City.
Enhanced Clutter Mitigation in Weather Radar Observations Through Comparison Between a Dual-Polarisation, Dual-Scan, and Dual-Polarisation Dual-Scan Ali Wardhana; Rizaldi Boer; Bambang Dwi Dasanto; Danang Eko Nuryanto; I Putu Santikayasa
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1219

Abstract

Ground clutter remains a significant source of contamination in weather radar observations, adversely affecting the interpretation of echoes and subsequent meteorological applications. This study assesses the Dual-Polarisation Dual-Scan (DPDS) framework. This Bayesian-based classifier combines polarimetric descriptors (ρₕᵥ, ZDR) with temporal coherence (ρ₁₂) derived from consecutive azimuthal scans. The analysis uses I/Q data from an operational dual-polarisation C-band weather radar located in Sidoarjo, Surabaya, Indonesia. Results indicate that the DPDS framework significantly outperforms traditional Dual-Polarisation (DP) and Dual-Scan (DS) methods. For moving weather (W), the DPDS achieved a Probability of Detection (POD) of 0.939, a 313-fold improvement over the DP-only method, which suffered from severe polarimetric overlap between clutter and rain. While the clutter class exhibited a False Alarm Ratio (FAR) of 0.749, this is attributed to the 83-second scan interval of the Sidoarjo radar; over this duration, stable tropical rain remains highly correlated, mimicking the temporal signature of stationary ground clutter (C). However, the framework successfully preserved the integrity of the meteorological field, reducing the misclassification of zero-velocity weather (W0) compared to DS-only methods and achieving an overall accuracy of 0.982. These findings highlight the effectiveness of integrating polarimetric and temporal decorrelation information to establish a more robust, physically consistent echo classification framework, particularly under challenging conditions of clutter and low-velocity weather.
Machine Learning-based Ground Motion Prediction Models for Indonesia: A Performance Evaluation of XGBoost and Random Forest Against Conventional GMPE Rayhan Irfan Hielmy; Edy Santoso; Muzli Muzli; Sigit Pramono; Setyoajie Prayoedhie
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1233

Abstract

Accurate ground motion prediction is critical for seismic hazard mitigation in Indonesia, a region characterized by complex tectonic settings. Conventional Ground Motion Prediction Equations (GMPEs) often struggle to capture the non-linear attenuation characteristics of local seismicity. This study develops national-scale Machine Learning (ML) models—specifically XGBoost and Random Forest (RF)—to predict Peak Ground Acceleration (PGA) and benchmarks them against the widely used Zhao et al. [23] model. The dataset comprises 20,287 strong-motion records from 1,573 events (M 1.6–7.9) recorded by 667 stations across Indonesia between January 2023 and April 2025. Performance evaluation reveals that the XGBoost model outperforms both RF and the conventional GMPE, achieving the lowest Mean Squared Error (MSE) of 0.8885. In contrast, the conventional model showed less reliability with a significantly higher MSE of 1.5905. Feature importance analysis indicates that hypocentral distance and magnitude are the dominant predictors, while site condition (Vs30) plays a secondary role. Validation on independent test sets and recent significant seismic events confirms the model's robust generalization capability compared to conventional approaches. These findings demonstrate that ML-based approaches provide a more reliable alternative for estimating ground motion in Indonesia, offering significant potential for improving early warning systems and hazard maps.

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